ENI Refining and Marketing (ENI
R&M), like many other operating companies, found itself
challenged to properly maintain its large, installed base of
existing advanced process control (APC) applications with a
reduced workforce. Frequent lube oil production changes were
being made to capitalize on supply chain opportunities. The
limited APC resources were struggling to keep up, as these
changes required updates to the controller models to ensure
that the APC solutions continued to generate the highest
value.

After reviewing new tools and
methodologies to improve efficiency, ENI R&M selected
performance monitoring, automated testing and adaptive modeling
tools for APC from a trusted technology provider. ENI
R&M tested the adaptive modeling tool at its Livorno refinery in Italy with positive
results, prompting the company to deploy adaptive modeling
programs at its other refineries.

ENI R&M Livorno refinery

The Livorno refinery is a
fuels and lube oil refinery with a significant number of
installed APC applications. Fig. 1 shows a simplified refinery
layout. The refinery runs 13 medium- to large-scale
model-predictive controllers (MPCs) and 24 inferential modeling
applications, for a total of 210 manipulated variables (MVs)
and 92 inferential properties.

Fig.
1. Simplified process flow diagram of
Livorno refinery.

Fig. 2 shows the refinerys
APC coverage. APC applications cover all major process units,
and additional controllers are planned for the remaining
plants. Since it is a lube oil refinery, Livornos
frequent lube oil production changes affect operations and,
therefore, the APC applications performance. This is a
significant change in addition to normal crude oil changes and
crude oil quality disturbances. For these reasons, APC maintenance for best performance is
an ongoing task that keeps the site APC engineer continually
engaged.

Fig.
2. APC coverage at the Livorno
refinery.

Sustaining APC benefits

It is something of a misnomer to
say that APC applications require maintenance. If nothing in
the plant ever changes, then almost no maintenance and no model
updates are required. However, if significant changes are made
to the process, or if feedstock characteristics change
significantly, then the APC models must be made
aware of these changes. When model updates are not
performed, or when regular controller maintenance is not
carried out due to significant process and instrumentation
changes, the performance of the APC system starts to
degrade.

There are many potential reasons
for performance degradation, but some of the most likely are
listed below:

Internal staff familiar with the application move to a
different position, and new staff may not be able to
immediately support the application or may require
significant training to understand and support it

Processes are often changed, and these changes can affect
controller performance

Catalyst changes, exchanger fouling and changes to valves
and other instrumentation can lead to degradation

Routine maintenance on instrumentation and equipment can
impact performance

Economic changes affect the steady-state solver
solutions, and if they are not recognized and
accommodated, performance may degrade or the controller
may lose money instead of accumulating profits.

Typical signs of performance
degradation are:

Sub-controllers in off status and MVs or
controlled variables (CVs) are routinely out of service
or in distributed control system (DCS) local
status

Some CVs never reach steady-state targets before these
targets change

Some CVs remain outside limits for extended periods

Many MV limits are clamped or MVs are at
setpointi.e., with high/low limits set to identical
values

Some MVs show noise response with frequent
change of direction

Almost all MVs in a controller are moving on every
controller execution

MV dynamics are often limited by the maximum move limit

CV prediction error tends to be positive and then
negative for extended periods, indicating model mismatch

The control engineer usually is not automatically alerted
to the problem

Operators will likely call for help only when the problem
becomes too severe to tolerate

The control engineer may spot the issue while checking
trends or controller limits, or when passing by the
control room.

3. Diagnose

At some point, the control engineer spots the issue or is
notified by a keen operator

The control engineer will attempt a manual diagnosis by
speaking with operators and analyzing data either online
or offline.

4. Repair

Diagnosis is completed

The problem may be ignored or manually repaired; often, a
sub-optimal solution is implemented (e.g., the controller
is de-tuned or gains are manually adjusted)

Small problems tend to build up until parts of the
controller or the entire application are switched off; a
major revamping step then must be undertaken.

The control engineer must often
manually extract process data to isolate the root cause. After
the nature of the problem has been determined, the manual
model-building method prolongs the amount of time needed to
correct the problem and return the controller to full service.
If maintenance is deferred, the problems slowly accumulate
until a major revamp must be undertaken. This approach is
inefficient, and it causes a loss of benefits that can be as
high as 50%60% during the four- to five-year application
lifecycle. With supporting automation, this workflow can be
significantly streamlined, and the time and effort needed to
keep the controllers at peak efficiency can be reduced.

Successful APC application
maintenance requires plans and practices to be aligned with
business strategy and supported by management, which ensures
that tools, people and processes are in sync (Fig. 3). A proper
APC maintenance methodology should have the following
characteristics:

Incorporates APC best practices

Minimizes effort by automating and simplifying
maintenance tasks

Uses proper baselines, as well as key performance
indicators (KPIs) covering both controllers and models

Uses automated reports to rapidly detect changes in
performance

Employs diagnostic rules to isolate root causes of
performance degradation and make quick assessments of
problems

Technology continues to improve,
and tools that enable a proactive maintenance methodology are
available on the market. With this kind of automation, the four
steps to maintain an APC application described previously can
now be performed differently, as depicted in Fig. 4.

Fig.
4. Automated APC maintenance
workflow.

Sustained value tools

The sustained value tools
supporting detection, diagnostics and repair are described
below.

Performance
monitoring. A proprietary performance-monitoring tool
has the capability to create a history of controller and
process data, build baselines, calculate controller and process
KPIs, and automate reporting. Using these performance KPIs, the
user can rapidly detect when the process is not operating at
peak performance. Model KPIs show the specific MV/CV pairs that
are contributing to poor performance.

Automated step
testing. An MPC is used to maintain the process within
specifications at all times. The automated step-testing tool
supports single-test and multi-test methods, and it produces
richer data more quickly than manual step testing, since it
enforces APC best practices and estimates the largest possible
MV steps while maintaining the process within constraints. Much
of the plant testing can now be performed without engineering
supervision.

All of this automated workflow is
performed online, from a web interface, directly on the running
controller. There is no need to start a data collection task,
extract data, move data between systems, model or tune offline,
or start or stop applications. The process is fully
streamlined, and it enforces APC best practices at all stages.
It also gives the APC engineer the capability to control and
influence the results while eliminating routine manual
activities.

The methodology is designed to
enable APC end-users to perform regular, proactive APC
maintenance on their own, without involving an external
consultant. End-users should hire an external consultant only
in the case of a major process revamp and never for routine
maintenance, since the tools and methodology now enable
non-experts to efficiently maintain APC applications.

Livorno refinery proof of concept

Among the Livorno refinerys
APC applications, there are two hot oil circuits: HOTOIL1 and
HOTOIL2. The first circuit delivers around 65 MM Kcal/h, and
the second delivers around 25 MM Kcal/h, to reboilers and other
exchangers in plants throughout the refinery. Fig. 5 shows a
simplified screenshot of the circuits.

Fig.
5. Simplified screenshot of hot oil
circuit
controllers.

The adaptive modeling evaluation
focused on the HOTOIL1 circuit controller, and specifically on
the F1 furnace. The HOTOIL1 controller design includes the
following attributes:

11 MVs, 54 CVs, and nearly 100% service factor

Most MVs are related to the F1 furnace

Most CVs are valve outputs of hot oil user control
loops

Controller was originally deployed in 2005.

Controller objectives and benefits

Operations flexibility and maximization of delivered
duty when required

Rejection of disturbances

Temperature and loop pressure stability

Optimization of furnace combustion.

Controller main constraints

Loop pressure and return temperature

Feed pump capacity

Furnace skin temperature, draft and excess
O2.

Likewise, the F1 furnace design
includes four cells, eight passes, mixed fuel gas/fuel oil
burners, four dampers and one blower with backup, as shown in
Fig. 6. An evaluation of the new tool and methodology was
conducted in a meeting room near the control room, with around
15 APC engineers from several ENI R&M refineries.

Fig.
6. Simplified screenshot of F1
furnace.

Efficiency control of the F1
furnacewhich uses a multivariable MPCwas found to
have been running with limited capability for some months, due
to model degradation after field equipment maintenance. The
service factor was still around 100%, but significant benefits
were left on the table. A model revamp for that section was
required, since the old models could not run on a closed-loop
system after the process changes. The furnace was found to be
an ideal candidate for an adaptive modeling pilot project.

Six MVs were involved in the
maintenance activity, which began with the scenario described
in Table 1. The two-day evaluation encompassed the following
steps:

Controller performance assessment through baselines and
KPIs

Automated step-testing tool configured and run throughout
the entire process

As-is MQ assessment performed

Automated data cleaning and case setup on the performance
monitoring system

Model ID iterations

Online model update and deployment

Post-revamp MQ assessment.

A virtual machine connected to the
ENI
R&M control network was used for the evaluation. All work
was done online from the production control web server operator
interface. During automated testing activity, the engineer
group had time to discuss maintenance methodology, and revise
baselines and KPIs.

The most interesting KPI that was
discussed and enabled is a modified version of the utilization
factor (UTL), which is available as part of a collection of
built-in KPIs in the refinerys performance
monitoring system. The idea of a UTL was first proposed by
Allan G. Kern in Hydrocarbon Processing in October
2005. This KPI, modified by ENI R&M engineers, is defined
as follows:

ENI_UTL = (CCS + MFU + MOK)
÷ IPMIND 3 100

where:
CCS = Number of CVs at high/low limit, setpoint, ramp or
external targets
MFU = Number of MVs at external target or engineering
limits
MOK = Number of MVs at minimum movement, wound up, in bad
status or taken out of service by the engineer
IPMIND = Actual number of MVs in the controller.

A favorable performance for this
KPI guarantees that the controller is not only on, but that it
is also moving and using all available MVs to push
constraintsi.e., to accumulate APC benefits. A multi-test
mode was used in the MPC from the beginning. This allowed the
MVs to be tested simultaneously to minimize step-testing time,
while minimizing MV correlation and maximizing the
signal-to-noise ratio to enhance MQ. As the automated tester
evaluated the unit, the group concentrated on adaptive modeling
usage and results, as outlined below:

View and clean up the MQ data

User can view the data used in the MQ analysis
evaluation

Some data cleaning is automatically performed

The engineer can manually clean the data further,
using a web viewer

Calculations for automated data cleaning can
beconfigured (e.g., when an MV is moved to DCS
control
or when a CV control error is too high).

Run an MQ test

Run the test from the web viewer

Schedule a recurring MQ test at a designated time and
interval

Model KPI carpet plots are automatically updated.

Configure and run a model ID case

Browse the performance monitors database for
tags to include in the model ID case

The ID case can be run on demand or scheduled to run
automatically, at a particular time and interval.

Review model and deploy

Multiple model ID cases can be compared with the
current model directly in the web viewer

Bode plot analysis is available in the web viewer, to
assess model uncertainty

Once satisfied, the model can be assembled and
deployed online.

All of these activities have been
carried out online, through a web interface, using data
available in the performance-monitoring database. MQ data
appear as a KPI plot where each model (MV/CV pair) is flagged
with different colors. The colors indicate how functional the
models used by the controller are compared to those assessed
with only a few MV moves. The complete model matrix is shown in
Fig. 7, and the models on which the project team concentrated are
highlighted in red within an oval.

Fig. 7. Complete model
matrix plot.

When an MQ case is executed, an
estimated gain multiplier (gmult) value is calculated in such a
way that the prediction errors of the corresponding dependent
variable are minimized. The estimated gmult will then include
contributions from the model uncertainty, not only in the
steady-state gain, but also in the accuracy of the
dynamics.

The patented MQ technology uses the existing
controller model as a reference to calculate an MQ index, which
is a combination of the estimated gmult value and the
calculated model uncertainty error bound. This index represents
the accuracy of the model pair in predicting the process
response:

Good (green) means the model pair has a high degree of
accuracy

Fair (light blue) means the accuracy is somewhere between
good and bad

Bad (red) means the model accuracy is low

Unknown (yellow) means a clear answer could not be
derived from the data provided, likely due to
insufficient significant data.

During the evaluation, focus was
placed on a small portion of the matrix, and MV steps were
performed in that portion; this is the reason that so many red
and yellow blocks can be seen in Fig. 7.

In a routine maintenance activity,
three to four steps should be performed for all relevant MVs
for MQ analysis. After assessing if and where models need
further improvement (via the MQ analysis), more steps should be
implemented for the models that need to be re-identified, and
the model ID results should be checked every few hours. Step
testing is only performed for the MVs for which new models are
needed, and only for as many as are required to obtain a
sufficiently accurate model. A proper maintenance routine will
require tests of only a few MVs, as models typically show some
local degradation following an event. It is uncommon for the
entire matrix to exhibit model accuracy issues.

Models can be inspected as step
responses or as bode plots, as shown for the HOTOIL1 controller
in Fig. 8. The starting model is shown in blue, while the newly
identified model (based on 20 hours of step testing) is shown
in pink. Note the substantial differences on the diagonal,
which is exactly where the MQ analysis previously reported the
model accuracy to be poor.

Fig. 8. Models can be
inspected as step
responses or as bode plots.

Bode plots have been useful in
monitoring modeling progress during step testing. In Fig. 9,
three hours of step-test data are compared against nearly 20
hours of step-test data. It can be seen that the uncertainty
bands become narrow, while the signal-to-noise ratio improves
as the step test proceeds.

Fig. 9. Step-test data for
three hours vs. 20 hours.

The evaluation was stopped after 24
hours of unattended step testing, and then the updated models
were replaced online from the web interface without needing to
restart the controller. The effects of models and tuning
changes can be directly checked online, through the production
control web server interface, using a what-if
simulation that permits a comparison between old and new
responses before deployment. Model quality was reassessed after
deployment to confirm the improvement, as shown in Fig.
10.

Fig. 10. Reassessment of
model matrix plot
shows quality improvement.

Results

The HOTOIL1 controller was brought
back into full operation at the end of the evaluation, with the
following significant results:

Correct operation for HOTOIL1s multivariable MPC
was restored, allowing for tighter control of excess
O2 and draft in F1 furnace cells

Operating target was increased for dampers and decreased
for blowers, since the updated models exhibited favorable
performances

Excess O2 was significantly reduced

F1 efficiency increased by 1.2%, on average, after the
revamp, which is significant for a 65-MM Kcal/h furnace
in terms of reduction in fuel consumption, and worth well
above 100,000/year at the current cost of fuel oil.

Advantages of the solution

The entire maintenance process is
performed online, directly from a web viewer and on the running
controller. It enforces best practices and moves maintenance
from reactive to proactive, thereby maximizing controller
uptime and benefits. Also, controller performance checkups
become a regular activity that requires limited effort.

With the use of the sustained value
tools, maintenance activities are triggered by a few properly
designed controller KPIs and model KPIs. These KPIs can be
easily compared against one or more baselines that can be
manually or automatically built in minutes. Automatic reports
can be scheduled and designed to include KPIs, calculations and
trends; these reports are then sent to operators, engineers and
managers.

KPI carpet plots, diagnostics and
drill-down capabilities enable control engineers to rapidly
detect and diagnose the problem, whether it is instrumentation,
DCS proportional-integral-derivative (PID) controller tuning,
MPC tuning, MPC design or MPC models. Fixing the problem is
then mostly automated (although still under engineer control),
but it avoids the need for time-consuming manual tasks or
controller downtime.

A streamlined APC maintenance
process with proper tools is now available to preserve APC
know-how, even with APC engineers moving into other positions.
Proactive maintenance prevents benefits degradation and nearly
eliminates the need for costly full-controller
revamps. It also permits the APC engineer to spot new
opportunities to increase delivered benefits.

Takeaway

The evaluation performed at ENI
R&Ms Livorno refinery clearly demonstrated the
validity of the methods and tools used. The HOTOIL1
multivariable MPC section was successfully revamped in only two
days through non-continuous work, with an automated tester
taking care of nighttime plant testing. Models were updated and
all MVs were put back in service, which delivered immediate and
significant benefits of greater than 100,000/year. Other
advantages included a faster model ID process due to the use of
adaptive modeling features, and the capability to run MQ
assessment and model ID from a web interface.

The maintenance activity was
completed in around 24 hours, with almost no engineering
supervision during step testing, and with plenty of time to
become familiar with the tools and technology. Time was
available to discuss what KPIs to put in place, and how to
improve controller performance.

The key lesson learned from the
experience was to spend available time optimizing operations
and increasing benefits, and not to execute repetitive tasks.
In a refinery with numerous APC applications, such as ENI
R&Ms Livorno facility, there are many opportunities
to improve performance, even with favorable onstream factors.
These opportunities are not always noted or taken advantage of,
however, due to a lack of proper tools and methodology. Also,
there is not always enough time to address them when conducting
work in the traditional way. HP

The authors

Stefano
Lodolo is a senior advisor and industry
consultant with Aspen Technology in Italy. He has more
than 25 years of APC field experience in the refining, chemical and petrochemical industries. Mr.
Lodolo has successfully implemented dozens of MPC and
other automation projects at a wide variety of process
units. He holds a masters degree in chemical
engineering from Bologna University in Italy.

Michael
Harmse is the senior director of APC product
management at Aspen Technology in Houston, Texas. He has
28 years of experience in process control, and has
completed 45 APC applications since 1994. He is the
inventor of the SmartStep constrained multivariable
testing technology and the SmartAudit co-linearity
detection and repair tool. He is listed as the principal
inventor on multiple US and EU patents. Mr. Harmse has
also introduced several new APC products: Aspen
SmartStep, Aspen PID Watch, Aspen Nonlinear Controller
(Aspen Apollo), Aspen Fuel Gas Optimizer and Aspen
State-Space Controller.

Andrea
Esposito is a senior APC engineer at ENI
R&Ms Livorno refinery in Italy. He is in
charge of project development and application maintenance for APC, as well
as automation at the DCS level. Before joining ENI in
2006, Mr. Esposito worked as a software engineer. He
has an engineering degree in telecommunications from
the University of Pisa in Italy.

Augusto
Autuori is responsible for APC project
coordination at ENI refineries. After earning his
bachelors degree in chemical engineering from the
University of Salerno in Italy, he joined ENI in 2002 as
an APC engineer. Between 2002 and 2006, Mr. Autuori
participated in several APC projects, including DMCplus
and inferential implementation. In 2006, he moved to the
technology department at ENI
R&Ms headquarters to manage APC project coordination, oil
movement systems implementation at ENIs primary logistics
hubs, innovative systems implementation for plant
monitoring, and operator training.

Have your say

All comments are subject to editorial review.
All fields are compulsory.